Biologic drugs, primarily comprising proteins and nucleic acids, have emerged as powerful therapeutic modalities; however, their discovery and optimization are often hindered by their inherent complexity. The advent of artificial intelligence (AI), particularly deep learning, is catalyzing a paradigm shift in this field, transitioning it from a process reliant on serendipity and laborious experimentation to a data-driven engineering discipline. This review systematically charts the co-evolution of AI methodologies and their transformative applications across the modern biologic drug development pipeline. We first outline AI’s methodological progression, from language models deciphering biological sequence grammar to structure prediction models like AlphaFold making macromolecular folds computationally accessible, and finally to generative models enabling de novo molecular creation. We then explore the practical impact of these technologies in two core phases: the de novo design of novel biologics with bespoke functions and the subsequent multi-parameter engineering and optimization of these candidates for clinical viability. While the potential is immense, significant strategic challenges remain, including the need to build a new AI-native experimental ecosystem and bridge the profound complexity gap between molecular-level predictions and systemic in vivo outcomes. Overcoming these obstacles will usher in a new era of AI-driven, automated closed-loop drug discovery.

Artificial intelligence  /  Biologic drugs  /  De novo design  /  Drug optimization  /  Drug delivery system  /  Nucleic acid therapeutics  /  Machine learning  /  Generative models